A scalar quantizer is defined by its decision levels, which determine the input data range that is mapped onto each quantized data interval, as well as by the reconstruction values corresponding to each quantized data interval. Quantization implies that the accuracy of the quantized data is reduced compared to the non-quantized data; this is done in order to reduce the amount of information contained in the data. In the decoder and the decoding method the “inverse” process of quantization takes place, which is called reconstruction or sometimes also de-quantization. Ideally, during reconstructing the parts of the signal that were removed during quantization are reconstructed to provide the original signal. Quantization is for instance part of any non-lossless data compression scheme. The quantized data is usually efficiently compressed into a compressed data signal. The compressed signal may e.g. be stored on a data carrier or be transmitted to a decoder/decompression unit.
An often used quantization is truncation of least significant bits. The process of truncating, i.e. deleting, least significant bits is a type of quantization in which a uniform quantizer is used with a step corresponding to the number of truncated LSBs. Truncation of Least significant bits is in essence quantization with a quantized data interval of 2n(2, 4, 8, 16 etc).
Quantization, for instance truncating a number of least significant bits before compression, reduces the amount of data to be transferred, at a loss in information transferred. During reconstruction the ‘lost’ values for the removed part of the data, for instance for the truncated Least Significant Bits, are reconstructed. The reconstruction value is in known methods typically chosen to be in the middle of the step size interval corresponding to a certain quantizer index or quantization step. This is the case for e.g. H.261, MPEG-1, MPEG-2/H.262, H.263, JPEG-2000, and MPEG-4 Part 2. In the case of intra-frame DCT coefficients, which are known to have a distribution that is peaked around zero, a value that is more biased towards zero typically provides better performance. The disadvantage of using known methods for encoding and decoding is that they often result in a considerably high distortion of the reconstructed data signal, which for instance in the case of an image signal results in distortions in the image, especially when a relatively large number of least significant bits (i.e. much of the original information in the input signal) are deleted. The quantization-reconstruction provides for errors.